PROJECT SUMMARY: Malignant carcinomas disseminate individual cells and multicellular clusters through coordinated processes of cellular detachment and motility. These mechanical cell-cell and cell-matrix interactions can vary heterogeneously across different cells and also vary plastically over time. However, cell-generated forces are often inferred indirectly from protein expression and cell morphology. Moreover, classical migration assays have limited capabilities for resolving single cell dynamics and utilize 2D monolayer culture with questionable relevance for the tumor microenvironment. Thus, there is a critical need for precision measurements of cell migration and mechanics in biomimetic 3D microenvironments. Our long-term goal is to develop technologies that enable biomechanical profiling of tumor invasion ex vivo and screen the response to targeted inhibitors. Technical challenges include 1) tracking rare events that result in invasive phenotypes, 2) resolving cell-generated forces, 3) utilizing 3D culture models and 4) scale-up for higher throughput assays. To address these challenges, the objective of this proposal is to profile single cell migration, morphology and mechanics in multicellular clusters embedded within 3D microenvironments. Our approach integrates two complementary techniques for precision measurement. PI: Wong has previously demonstrated automated and comprehensive single cell tracking of collective and individual migration. Co-I: Franck has demonstrated 3D traction force microscopy (TFM) and 3D mean deformation metrics (MDM) for cell-generated matrix deformations. With the support of this IMAT R21, we will develop this approach in a model system based on inducing the epithelial-mesenchymal transition (EMT) in 3D multicellular clusters. AIM 1 will develop quantitative analyses and metrics for profiling phenotypic heterogeneity and plasticity during the transition to invasion. Once established, AIM 2 will scale up this approach for high-content screening in a 96 well plate. We will first implement computationally efficient algorithms to correct for inconsistencies in stage positioning using graphics processing units (GPUs). Next, we will screen panels of targeted inhibitors against migration and EMT at varying concentrations to measure how single cell migration and mechanics are perturbed. We envision this technology will reveal new fundamental insights into how cancer cells aberrantly interact as a complex system. Moreover, this integrated approach can be applied to patient samples ex vivo as an early, label-free prognostic indicator, to predict drug response and for preclinical therapeutic screens.